Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.3 KiB
Average record size in memory249.3 B

Variable types

Categorical8
Text1
Numeric22

Alerts

Price is highly overall correlated with profit and 2 other fieldsHigh correlation
fill_rate is highly overall correlated with no_orders_notful and 3 other fieldsHigh correlation
no_orders_notful is highly overall correlated with fill_rate and 5 other fieldsHigh correlation
no_prod_sold is highly overall correlated with fill_rate and 5 other fieldsHigh correlation
order_qun is highly overall correlated with fill_rate and 1 other fieldsHigh correlation
other_cost is highly overall correlated with total_costHigh correlation
profit is highly overall correlated with Price and 4 other fieldsHigh correlation
profit_margin is highly overall correlated with Price and 4 other fieldsHigh correlation
revenue is highly overall correlated with Price and 4 other fieldsHigh correlation
stockout_rate is highly overall correlated with fill_rate and 3 other fieldsHigh correlation
total_cost is highly overall correlated with other_costHigh correlation
SKU has unique values Unique
Price has unique values Unique
rev_gen has unique values Unique
shipping_cost has unique values Unique
manfuct_cost has unique values Unique
def_rate has unique values Unique
other_cost has unique values Unique
revenue has unique values Unique
total_cost has unique values Unique
profit has unique values Unique
profit_margin has unique values Unique
stockout_rate has unique values Unique
fill_rate has unique values Unique

Reproduction

Analysis started2025-10-01 20:01:07.490630
Analysis finished2025-10-01 20:01:54.032570
Duration46.54 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

p_type
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
skincare
40 
haircare
34 
cosmetics
26 

Length

Max length9
Median length8
Mean length8.26
Min length8

Characters and Unicode

Total characters826
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhaircare
2nd rowskincare
3rd rowhaircare
4th rowskincare
5th rowskincare

Common Values

ValueCountFrequency (%)
skincare 40
40.0%
haircare 34
34.0%
cosmetics 26
26.0%

Length

2025-10-01T23:01:54.121547image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T23:01:54.183297image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
skincare 40
40.0%
haircare 34
34.0%
cosmetics 26
26.0%

Most occurring characters

ValueCountFrequency (%)
c 126
15.3%
a 108
13.1%
r 108
13.1%
i 100
12.1%
e 100
12.1%
s 92
11.1%
k 40
 
4.8%
n 40
 
4.8%
h 34
 
4.1%
o 26
 
3.1%
Other values (2) 52
6.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 826
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 126
15.3%
a 108
13.1%
r 108
13.1%
i 100
12.1%
e 100
12.1%
s 92
11.1%
k 40
 
4.8%
n 40
 
4.8%
h 34
 
4.1%
o 26
 
3.1%
Other values (2) 52
6.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 826
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 126
15.3%
a 108
13.1%
r 108
13.1%
i 100
12.1%
e 100
12.1%
s 92
11.1%
k 40
 
4.8%
n 40
 
4.8%
h 34
 
4.1%
o 26
 
3.1%
Other values (2) 52
6.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 826
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 126
15.3%
a 108
13.1%
r 108
13.1%
i 100
12.1%
e 100
12.1%
s 92
11.1%
k 40
 
4.8%
n 40
 
4.8%
h 34
 
4.1%
o 26
 
3.1%
Other values (2) 52
6.3%

SKU
Text

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2025-10-01T23:01:54.706102image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length4.9
Min length4

Characters and Unicode

Total characters490
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowSKU0
2nd rowSKU1
3rd rowSKU2
4th rowSKU3
5th rowSKU4
ValueCountFrequency (%)
sku0 1
 
1.0%
sku12 1
 
1.0%
sku2 1
 
1.0%
sku3 1
 
1.0%
sku4 1
 
1.0%
sku5 1
 
1.0%
sku6 1
 
1.0%
sku7 1
 
1.0%
sku8 1
 
1.0%
sku9 1
 
1.0%
Other values (90) 90
90.0%
2025-10-01T23:01:55.413721image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
S 100
20.4%
K 100
20.4%
U 100
20.4%
6 20
 
4.1%
3 20
 
4.1%
7 20
 
4.1%
2 20
 
4.1%
1 20
 
4.1%
9 20
 
4.1%
8 20
 
4.1%
Other values (3) 50
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 490
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 100
20.4%
K 100
20.4%
U 100
20.4%
6 20
 
4.1%
3 20
 
4.1%
7 20
 
4.1%
2 20
 
4.1%
1 20
 
4.1%
9 20
 
4.1%
8 20
 
4.1%
Other values (3) 50
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 490
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 100
20.4%
K 100
20.4%
U 100
20.4%
6 20
 
4.1%
3 20
 
4.1%
7 20
 
4.1%
2 20
 
4.1%
1 20
 
4.1%
9 20
 
4.1%
8 20
 
4.1%
Other values (3) 50
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 490
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 100
20.4%
K 100
20.4%
U 100
20.4%
6 20
 
4.1%
3 20
 
4.1%
7 20
 
4.1%
2 20
 
4.1%
1 20
 
4.1%
9 20
 
4.1%
8 20
 
4.1%
Other values (3) 50
10.2%

Price
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.462461
Minimum1.699976
Maximum99.171329
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:55.528403image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1.699976
5-th percentile4.0507218
Q119.597823
median51.23983
Q377.198228
95-th percentile96.396366
Maximum99.171329
Range97.471353
Interquartile range (IQR)57.600405

Descriptive statistics

Standard deviation31.168193
Coefficient of variation (CV)0.63013833
Kurtosis-1.3734706
Mean49.462461
Median Absolute Deviation (MAD)28.292667
Skewness-0.022538919
Sum4946.2461
Variance971.45624
MonotonicityNot monotonic
2025-10-01T23:01:55.841619image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69.80800554 1
 
1.0%
13.01737679 1
 
1.0%
83.85101768 1
 
1.0%
90.20442752 1
 
1.0%
6.381533163 1
 
1.0%
47.91454182 1
 
1.0%
54.86552852 1
 
1.0%
37.93181238 1
 
1.0%
87.75543235 1
 
1.0%
26.03486977 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.699976014 1
1.0%
2.397274706 1
1.0%
3.037688725 1
1.0%
3.170011414 1
1.0%
3.526111259 1
1.0%
4.078332863 1
1.0%
4.156308359 1
1.0%
4.324341186 1
1.0%
4.805496036 1
1.0%
6.306883176 1
1.0%
ValueCountFrequency (%)
99.17132864 1
1.0%
99.11329162 1
1.0%
98.03182966 1
1.0%
97.76008558 1
1.0%
97.44694662 1
1.0%
96.34107244 1
1.0%
95.71213588 1
1.0%
92.99688423 1
1.0%
92.55736081 1
1.0%
91.12831835 1
1.0%

avail_per
Real number (ℝ)

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.4
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:56.020506image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q122.75
median43.5
Q375
95-th percentile96.05
Maximum100
Range99
Interquartile range (IQR)52.25

Descriptive statistics

Standard deviation30.743317
Coefficient of variation (CV)0.63519249
Kurtosis-1.3319932
Mean48.4
Median Absolute Deviation (MAD)27.5
Skewness0.18361821
Sum4840
Variance945.15152
MonotonicityNot monotonic
2025-10-01T23:01:56.167367image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 4
 
4.0%
55 3
 
3.0%
75 3
 
3.0%
29 3
 
3.0%
16 3
 
3.0%
56 3
 
3.0%
23 3
 
3.0%
90 3
 
3.0%
26 3
 
3.0%
34 3
 
3.0%
Other values (53) 69
69.0%
ValueCountFrequency (%)
1 2
2.0%
3 1
 
1.0%
5 2
2.0%
6 1
 
1.0%
9 2
2.0%
10 2
2.0%
11 4
4.0%
12 1
 
1.0%
13 1
 
1.0%
14 2
2.0%
ValueCountFrequency (%)
100 1
 
1.0%
99 1
 
1.0%
98 1
 
1.0%
97 2
2.0%
96 1
 
1.0%
95 2
2.0%
94 1
 
1.0%
93 2
2.0%
91 1
 
1.0%
90 3
3.0%

no_prod_sold
Real number (ℝ)

High correlation 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean460.99
Minimum8
Maximum996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:56.294774image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile60.5
Q1184.25
median392.5
Q3704.25
95-th percentile960.15
Maximum996
Range988
Interquartile range (IQR)520

Descriptive statistics

Standard deviation303.78007
Coefficient of variation (CV)0.65897324
Kurtosis-1.2513936
Mean460.99
Median Absolute Deviation (MAD)241.5
Skewness0.28141802
Sum46099
Variance92282.333
MonotonicityNot monotonic
2025-10-01T23:01:56.429928image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
336 2
 
2.0%
320 2
 
2.0%
134 2
 
2.0%
963 2
 
2.0%
246 1
 
1.0%
637 1
 
1.0%
32 1
 
1.0%
511 1
 
1.0%
163 1
 
1.0%
513 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
8 1
1.0%
24 1
1.0%
25 1
1.0%
29 1
1.0%
32 1
1.0%
62 1
1.0%
65 1
1.0%
79 1
1.0%
83 1
1.0%
93 1
1.0%
ValueCountFrequency (%)
996 1
1.0%
987 1
1.0%
980 1
1.0%
963 2
2.0%
960 1
1.0%
946 1
1.0%
933 1
1.0%
919 1
1.0%
916 1
1.0%
913 1
1.0%

rev_gen
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5776.0482
Minimum1061.6185
Maximum9866.4655
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:56.545094image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1061.6185
5-th percentile1835.248
Q12812.8472
median6006.352
Q38253.9769
95-th percentile9571.8604
Maximum9866.4655
Range8804.8469
Interquartile range (IQR)5441.1298

Descriptive statistics

Standard deviation2732.8417
Coefficient of variation (CV)0.47313347
Kurtosis-1.4175921
Mean5776.0482
Median Absolute Deviation (MAD)2472.9427
Skewness-0.17382939
Sum577604.82
Variance7468424
MonotonicityNot monotonic
2025-10-01T23:01:56.682462image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8661.996792 1
 
1.0%
4256.949141 1
 
1.0%
7910.886916 1
 
1.0%
2633.121981 1
 
1.0%
8180.337085 1
 
1.0%
7014.887987 1
 
1.0%
1752.381087 1
 
1.0%
3550.218433 1
 
1.0%
9473.798033 1
 
1.0%
8367.721618 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1061.618523 1
1.0%
1229.591029 1
1.0%
1292.458418 1
1.0%
1605.8669 1
1.0%
1752.381087 1
1.0%
1839.609426 1
1.0%
1889.07359 1
1.0%
1912.465663 1
1.0%
1935.206794 1
1.0%
2021.14981 1
1.0%
ValueCountFrequency (%)
9866.465458 1
1.0%
9692.31804 1
1.0%
9655.135103 1
1.0%
9592.63357 1
1.0%
9577.749626 1
1.0%
9571.550487 1
1.0%
9473.798033 1
1.0%
9444.742033 1
1.0%
9435.762609 1
1.0%
9364.673505 1
1.0%

gender
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Unknown
31 
Female
25 
Non-binary
23 
Male
21 

Length

Max length10
Median length7
Mean length6.81
Min length4

Characters and Unicode

Total characters681
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNon-binary
2nd rowFemale
3rd rowUnknown
4th rowNon-binary
5th rowNon-binary

Common Values

ValueCountFrequency (%)
Unknown 31
31.0%
Female 25
25.0%
Non-binary 23
23.0%
Male 21
21.0%

Length

2025-10-01T23:01:56.815220image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T23:01:56.883097image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
unknown 31
31.0%
female 25
25.0%
non-binary 23
23.0%
male 21
21.0%

Most occurring characters

ValueCountFrequency (%)
n 139
20.4%
e 71
10.4%
a 69
10.1%
o 54
 
7.9%
l 46
 
6.8%
U 31
 
4.6%
w 31
 
4.6%
k 31
 
4.6%
F 25
 
3.7%
m 25
 
3.7%
Other values (7) 159
23.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 139
20.4%
e 71
10.4%
a 69
10.1%
o 54
 
7.9%
l 46
 
6.8%
U 31
 
4.6%
w 31
 
4.6%
k 31
 
4.6%
F 25
 
3.7%
m 25
 
3.7%
Other values (7) 159
23.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 139
20.4%
e 71
10.4%
a 69
10.1%
o 54
 
7.9%
l 46
 
6.8%
U 31
 
4.6%
w 31
 
4.6%
k 31
 
4.6%
F 25
 
3.7%
m 25
 
3.7%
Other values (7) 159
23.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 139
20.4%
e 71
10.4%
a 69
10.1%
o 54
 
7.9%
l 46
 
6.8%
U 31
 
4.6%
w 31
 
4.6%
k 31
 
4.6%
F 25
 
3.7%
m 25
 
3.7%
Other values (7) 159
23.3%

stock_per
Real number (ℝ)

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.77
Minimum0
Maximum100
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:56.985421image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q116.75
median47.5
Q373
95-th percentile97
Maximum100
Range100
Interquartile range (IQR)56.25

Descriptive statistics

Standard deviation31.369372
Coefficient of variation (CV)0.65667514
Kurtosis-1.2343435
Mean47.77
Median Absolute Deviation (MAD)29
Skewness0.10159282
Sum4777
Variance984.03747
MonotonicityNot monotonic
2025-10-01T23:01:57.110278image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 5
 
5.0%
90 4
 
4.0%
48 3
 
3.0%
100 3
 
3.0%
10 3
 
3.0%
4 3
 
3.0%
96 2
 
2.0%
60 2
 
2.0%
42 2
 
2.0%
57 2
 
2.0%
Other values (55) 71
71.0%
ValueCountFrequency (%)
0 1
 
1.0%
1 2
 
2.0%
2 1
 
1.0%
4 3
3.0%
5 5
5.0%
6 1
 
1.0%
9 1
 
1.0%
10 3
3.0%
11 1
 
1.0%
12 1
 
1.0%
ValueCountFrequency (%)
100 3
3.0%
98 1
 
1.0%
97 2
2.0%
96 2
2.0%
95 1
 
1.0%
93 2
2.0%
92 1
 
1.0%
90 4
4.0%
89 1
 
1.0%
86 1
 
1.0%

customer_lt
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.96
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:57.234196image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q18
median17
Q324
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.7858012
Coefficient of variation (CV)0.5504888
Kurtosis-1.1888488
Mean15.96
Median Absolute Deviation (MAD)8
Skewness-0.12983854
Sum1596
Variance77.190303
MonotonicityNot monotonic
2025-10-01T23:01:57.337104image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 6
 
6.0%
27 6
 
6.0%
17 6
 
6.0%
25 5
 
5.0%
19 5
 
5.0%
23 5
 
5.0%
8 5
 
5.0%
29 5
 
5.0%
26 5
 
5.0%
18 4
 
4.0%
Other values (19) 48
48.0%
ValueCountFrequency (%)
1 6
6.0%
2 3
3.0%
3 1
 
1.0%
4 2
 
2.0%
5 4
4.0%
6 2
 
2.0%
7 3
3.0%
8 5
5.0%
9 2
 
2.0%
10 3
3.0%
ValueCountFrequency (%)
30 2
 
2.0%
29 5
5.0%
28 1
 
1.0%
27 6
6.0%
26 5
5.0%
25 5
5.0%
24 3
3.0%
23 5
5.0%
22 2
 
2.0%
20 2
 
2.0%

order_qun
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.22
Minimum1
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:57.487784image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q126
median52
Q371.25
95-th percentile88
Maximum96
Range95
Interquartile range (IQR)45.25

Descriptive statistics

Standard deviation26.784429
Coefficient of variation (CV)0.54417776
Kurtosis-1.1192733
Mean49.22
Median Absolute Deviation (MAD)23.5
Skewness-0.10737313
Sum4922
Variance717.40566
MonotonicityNot monotonic
2025-10-01T23:01:57.651947image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85 6
 
6.0%
72 4
 
4.0%
66 4
 
4.0%
51 3
 
3.0%
26 3
 
3.0%
11 3
 
3.0%
52 3
 
3.0%
27 3
 
3.0%
22 3
 
3.0%
96 2
 
2.0%
Other values (51) 66
66.0%
ValueCountFrequency (%)
1 1
 
1.0%
2 1
 
1.0%
4 1
 
1.0%
6 1
 
1.0%
7 2
2.0%
8 1
 
1.0%
9 2
2.0%
10 2
2.0%
11 3
3.0%
15 1
 
1.0%
ValueCountFrequency (%)
96 2
 
2.0%
95 1
 
1.0%
94 1
 
1.0%
88 2
 
2.0%
85 6
6.0%
83 2
 
2.0%
82 1
 
1.0%
80 2
 
2.0%
78 2
 
2.0%
77 1
 
1.0%

shipping_lt
Real number (ℝ)

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.75
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:57.774184image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.75
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4.25

Descriptive statistics

Standard deviation2.7242829
Coefficient of variation (CV)0.47378833
Kurtosis-1.0712955
Mean5.75
Median Absolute Deviation (MAD)2
Skewness-0.2815893
Sum575
Variance7.4217172
MonotonicityNot monotonic
2025-10-01T23:01:57.891780image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 16
16.0%
7 14
14.0%
9 11
11.0%
4 10
10.0%
6 10
10.0%
3 10
10.0%
1 10
10.0%
5 8
8.0%
10 6
 
6.0%
2 5
 
5.0%
ValueCountFrequency (%)
1 10
10.0%
2 5
 
5.0%
3 10
10.0%
4 10
10.0%
5 8
8.0%
6 10
10.0%
7 14
14.0%
8 16
16.0%
9 11
11.0%
10 6
 
6.0%
ValueCountFrequency (%)
10 6
 
6.0%
9 11
11.0%
8 16
16.0%
7 14
14.0%
6 10
10.0%
5 8
8.0%
4 10
10.0%
3 10
10.0%
2 5
 
5.0%
1 10
10.0%

shipping_carr
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Carrier B
43 
Carrier C
29 
Carrier A
28 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters900
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCarrier B
2nd rowCarrier A
3rd rowCarrier B
4th rowCarrier C
5th rowCarrier A

Common Values

ValueCountFrequency (%)
Carrier B 43
43.0%
Carrier C 29
29.0%
Carrier A 28
28.0%

Length

2025-10-01T23:01:58.003733image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T23:01:58.095893image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
carrier 100
50.0%
b 43
21.5%
c 29
 
14.5%
a 28
 
14.0%

Most occurring characters

ValueCountFrequency (%)
r 300
33.3%
C 129
14.3%
a 100
 
11.1%
i 100
 
11.1%
e 100
 
11.1%
100
 
11.1%
B 43
 
4.8%
A 28
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 900
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 300
33.3%
C 129
14.3%
a 100
 
11.1%
i 100
 
11.1%
e 100
 
11.1%
100
 
11.1%
B 43
 
4.8%
A 28
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 900
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 300
33.3%
C 129
14.3%
a 100
 
11.1%
i 100
 
11.1%
e 100
 
11.1%
100
 
11.1%
B 43
 
4.8%
A 28
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 900
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 300
33.3%
C 129
14.3%
a 100
 
11.1%
i 100
 
11.1%
e 100
 
11.1%
100
 
11.1%
B 43
 
4.8%
A 28
 
3.1%

shipping_cost
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5481491
Minimum1.0134866
Maximum9.9298162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:58.255767image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1.0134866
5-th percentile1.4055747
Q13.5402477
median5.320534
Q37.6016949
95-th percentile9.5745308
Maximum9.9298162
Range8.9163297
Interquartile range (IQR)4.0614472

Descriptive statistics

Standard deviation2.6513755
Coefficient of variation (CV)0.47788469
Kurtosis-1.1835666
Mean5.5481491
Median Absolute Deviation (MAD)2.231315
Skewness-0.053738287
Sum554.81491
Variance7.0297922
MonotonicityNot monotonic
2025-10-01T23:01:58.457104image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.956572139 1
 
1.0%
2.457933528 1
 
1.0%
1.512936837 1
 
1.0%
6.59961416 1
 
1.0%
9.228190317 1
 
1.0%
6.315717755 1
 
1.0%
9.70528679 1
 
1.0%
1.194251865 1
 
1.0%
9.147811545 1
 
1.0%
2.216142729 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.013486566 1
1.0%
1.019487571 1
1.0%
1.194251865 1
1.0%
1.311023756 1
1.0%
1.32527401 1
1.0%
1.409801095 1
1.0%
1.45430531 1
1.0%
1.512936837 1
1.0%
1.532655274 1
1.0%
1.729568564 1
1.0%
ValueCountFrequency (%)
9.929816245 1
1.0%
9.898140508 1
1.0%
9.741291689 1
1.0%
9.716574771 1
1.0%
9.70528679 1
1.0%
9.567648921 1
1.0%
9.537283061 1
1.0%
9.235931437 1
1.0%
9.228190317 1
1.0%
9.160558535 1
1.0%

sup_name
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Supplier 1
27 
Supplier 2
22 
Supplier 5
18 
Supplier 4
18 
Supplier 3
15 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1000
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSupplier 3
2nd rowSupplier 3
3rd rowSupplier 1
4th rowSupplier 5
5th rowSupplier 1

Common Values

ValueCountFrequency (%)
Supplier 1 27
27.0%
Supplier 2 22
22.0%
Supplier 5 18
18.0%
Supplier 4 18
18.0%
Supplier 3 15
15.0%

Length

2025-10-01T23:01:58.611192image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T23:01:58.715971image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
supplier 100
50.0%
1 27
 
13.5%
2 22
 
11.0%
5 18
 
9.0%
4 18
 
9.0%
3 15
 
7.5%

Most occurring characters

ValueCountFrequency (%)
p 200
20.0%
S 100
10.0%
u 100
10.0%
l 100
10.0%
i 100
10.0%
e 100
10.0%
r 100
10.0%
100
10.0%
1 27
 
2.7%
2 22
 
2.2%
Other values (3) 51
 
5.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 200
20.0%
S 100
10.0%
u 100
10.0%
l 100
10.0%
i 100
10.0%
e 100
10.0%
r 100
10.0%
100
10.0%
1 27
 
2.7%
2 22
 
2.2%
Other values (3) 51
 
5.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 200
20.0%
S 100
10.0%
u 100
10.0%
l 100
10.0%
i 100
10.0%
e 100
10.0%
r 100
10.0%
100
10.0%
1 27
 
2.7%
2 22
 
2.2%
Other values (3) 51
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 200
20.0%
S 100
10.0%
u 100
10.0%
l 100
10.0%
i 100
10.0%
e 100
10.0%
r 100
10.0%
100
10.0%
1 27
 
2.7%
2 22
 
2.2%
Other values (3) 51
 
5.1%

delv_location
Categorical

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Kolkata
25 
Mumbai
22 
Chennai
20 
Bangalore
18 
Delhi
15 

Length

Max length9
Median length7
Mean length6.84
Min length5

Characters and Unicode

Total characters684
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai
2nd rowMumbai
3rd rowMumbai
4th rowKolkata
5th rowDelhi

Common Values

ValueCountFrequency (%)
Kolkata 25
25.0%
Mumbai 22
22.0%
Chennai 20
20.0%
Bangalore 18
18.0%
Delhi 15
15.0%

Length

2025-10-01T23:01:58.859628image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T23:01:58.991379image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
kolkata 25
25.0%
mumbai 22
22.0%
chennai 20
20.0%
bangalore 18
18.0%
delhi 15
15.0%

Most occurring characters

ValueCountFrequency (%)
a 128
18.7%
l 58
 
8.5%
n 58
 
8.5%
i 57
 
8.3%
e 53
 
7.7%
o 43
 
6.3%
h 35
 
5.1%
K 25
 
3.7%
k 25
 
3.7%
t 25
 
3.7%
Other values (9) 177
25.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 128
18.7%
l 58
 
8.5%
n 58
 
8.5%
i 57
 
8.3%
e 53
 
7.7%
o 43
 
6.3%
h 35
 
5.1%
K 25
 
3.7%
k 25
 
3.7%
t 25
 
3.7%
Other values (9) 177
25.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 128
18.7%
l 58
 
8.5%
n 58
 
8.5%
i 57
 
8.3%
e 53
 
7.7%
o 43
 
6.3%
h 35
 
5.1%
K 25
 
3.7%
k 25
 
3.7%
t 25
 
3.7%
Other values (9) 177
25.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 128
18.7%
l 58
 
8.5%
n 58
 
8.5%
i 57
 
8.3%
e 53
 
7.7%
o 43
 
6.3%
h 35
 
5.1%
K 25
 
3.7%
k 25
 
3.7%
t 25
 
3.7%
Other values (9) 177
25.9%

suppling_lt
Real number (ℝ)

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.08
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:59.127095image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q110
median18
Q325
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8462513
Coefficient of variation (CV)0.5179304
Kurtosis-1.1745173
Mean17.08
Median Absolute Deviation (MAD)7.5
Skewness-0.32620585
Sum1708
Variance78.256162
MonotonicityNot monotonic
2025-10-01T23:01:59.239542image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
18 9
 
9.0%
24 6
 
6.0%
10 6
 
6.0%
28 6
 
6.0%
25 6
 
6.0%
29 5
 
5.0%
26 5
 
5.0%
21 4
 
4.0%
4 4
 
4.0%
1 4
 
4.0%
Other values (19) 45
45.0%
ValueCountFrequency (%)
1 4
4.0%
2 2
 
2.0%
3 3
3.0%
4 4
4.0%
5 3
3.0%
6 1
 
1.0%
7 2
 
2.0%
8 2
 
2.0%
9 2
 
2.0%
10 6
6.0%
ValueCountFrequency (%)
30 2
 
2.0%
29 5
5.0%
28 6
6.0%
27 3
3.0%
26 5
5.0%
25 6
6.0%
24 6
6.0%
23 3
3.0%
22 3
3.0%
21 4
4.0%

prod_volum
Real number (ℝ)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean567.84
Minimum104
Maximum985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:59.364059image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile172.9
Q1352
median568.5
Q3797
95-th percentile953.1
Maximum985
Range881
Interquartile range (IQR)445

Descriptive statistics

Standard deviation263.04686
Coefficient of variation (CV)0.46324116
Kurtosis-1.2932782
Mean567.84
Median Absolute Deviation (MAD)230
Skewness-0.076547131
Sum56784
Variance69193.651
MonotonicityNot monotonic
2025-10-01T23:01:59.495219image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
791 2
 
2.0%
671 2
 
2.0%
867 2
 
2.0%
171 2
 
2.0%
215 1
 
1.0%
358 1
 
1.0%
444 1
 
1.0%
152 1
 
1.0%
258 1
 
1.0%
775 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
104 1
1.0%
109 1
1.0%
152 1
1.0%
171 2
2.0%
173 1
1.0%
176 1
1.0%
177 1
1.0%
179 1
1.0%
198 1
1.0%
202 1
1.0%
ValueCountFrequency (%)
985 1
1.0%
971 1
1.0%
964 1
1.0%
963 1
1.0%
955 1
1.0%
953 1
1.0%
937 1
1.0%
934 1
1.0%
929 1
1.0%
921 1
1.0%

manfuct_lt
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.77
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:59.608372image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q17
median14
Q323
95-th percentile29
Maximum30
Range29
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9124303
Coefficient of variation (CV)0.60341437
Kurtosis-1.2944597
Mean14.77
Median Absolute Deviation (MAD)7.5
Skewness0.18499721
Sum1477
Variance79.431414
MonotonicityNot monotonic
2025-10-01T23:01:59.711850image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
7 8
 
8.0%
28 7
 
7.0%
11 6
 
6.0%
23 5
 
5.0%
18 5
 
5.0%
4 5
 
5.0%
5 5
 
5.0%
29 4
 
4.0%
10 4
 
4.0%
21 4
 
4.0%
Other values (20) 47
47.0%
ValueCountFrequency (%)
1 3
 
3.0%
2 3
 
3.0%
3 3
 
3.0%
4 5
5.0%
5 5
5.0%
6 3
 
3.0%
7 8
8.0%
8 3
 
3.0%
9 2
 
2.0%
10 4
4.0%
ValueCountFrequency (%)
30 2
 
2.0%
29 4
4.0%
28 7
7.0%
27 2
 
2.0%
26 2
 
2.0%
25 2
 
2.0%
24 3
3.0%
23 5
5.0%
22 1
 
1.0%
21 4
4.0%

manfuct_cost
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.266693
Minimum1.0850686
Maximum99.466109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:01:59.825946image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum1.0850686
5-th percentile5.7820993
Q122.983299
median45.905622
Q368.621026
95-th percentile97.113967
Maximum99.466109
Range98.38104
Interquartile range (IQR)45.637726

Descriptive statistics

Standard deviation28.982841
Coefficient of variation (CV)0.61317683
Kurtosis-1.0923693
Mean47.266693
Median Absolute Deviation (MAD)23.065387
Skewness0.19149769
Sum4726.6693
Variance840.00509
MonotonicityNot monotonic
2025-10-01T23:01:59.945568image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
46.27987924 1
 
1.0%
20.07500398 1
 
1.0%
46.8702388 1
 
1.0%
55.7604929 1
 
1.0%
30.66167748 1
 
1.0%
11.44078182 1
 
1.0%
77.62776581 1
 
1.0%
97.11358156 1
 
1.0%
7.057876147 1
 
1.0%
42.08443674 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.08506857 1
1.0%
1.597222743 1
1.0%
1.900762244 1
1.0%
4.465278435 1
1.0%
5.604690864 1
1.0%
5.79143663 1
1.0%
5.930693646 1
1.0%
7.057876147 1
1.0%
8.693042426 1
1.0%
9.005807429 1
1.0%
ValueCountFrequency (%)
99.4661086 1
1.0%
98.60995724 1
1.0%
97.82905011 1
1.0%
97.7305938 1
1.0%
97.12128175 1
1.0%
97.11358156 1
1.0%
96.52735279 1
1.0%
96.42282064 1
1.0%
95.33206455 1
1.0%
92.0651606 1
1.0%

inspect_resl
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Pending
41 
Fail
36 
Pass
23 

Length

Max length7
Median length4
Mean length5.23
Min length4

Characters and Unicode

Total characters523
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPending
2nd rowPending
3rd rowPending
4th rowFail
5th rowFail

Common Values

ValueCountFrequency (%)
Pending 41
41.0%
Fail 36
36.0%
Pass 23
23.0%

Length

2025-10-01T23:02:00.081750image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T23:02:00.152990image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
pending 41
41.0%
fail 36
36.0%
pass 23
23.0%

Most occurring characters

ValueCountFrequency (%)
n 82
15.7%
i 77
14.7%
P 64
12.2%
a 59
11.3%
s 46
8.8%
e 41
7.8%
d 41
7.8%
g 41
7.8%
F 36
6.9%
l 36
6.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 523
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 82
15.7%
i 77
14.7%
P 64
12.2%
a 59
11.3%
s 46
8.8%
e 41
7.8%
d 41
7.8%
g 41
7.8%
F 36
6.9%
l 36
6.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 523
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 82
15.7%
i 77
14.7%
P 64
12.2%
a 59
11.3%
s 46
8.8%
e 41
7.8%
d 41
7.8%
g 41
7.8%
F 36
6.9%
l 36
6.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 523
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 82
15.7%
i 77
14.7%
P 64
12.2%
a 59
11.3%
s 46
8.8%
e 41
7.8%
d 41
7.8%
g 41
7.8%
F 36
6.9%
l 36
6.9%

def_rate
Real number (ℝ)

Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.277158
Minimum0.018607568
Maximum4.9392553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:02:00.258805image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.018607568
5-th percentile0.13045871
Q11.00965
median2.1418627
Q33.5639953
95-th percentile4.7470562
Maximum4.9392553
Range4.9206477
Interquartile range (IQR)2.5543454

Descriptive statistics

Standard deviation1.4613655
Coefficient of variation (CV)0.64174974
Kurtosis-1.1139982
Mean2.277158
Median Absolute Deviation (MAD)1.2702879
Skewness0.12964432
Sum227.7158
Variance2.1355893
MonotonicityNot monotonic
2025-10-01T23:02:00.566556image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.226410361 1
 
1.0%
3.63284329 1
 
1.0%
4.620546065 1
 
1.0%
3.213329607 1
 
1.0%
2.078750608 1
 
1.0%
1.830575599 1
 
1.0%
1.362387989 1
 
1.0%
1.983467872 1
 
1.0%
0.131955444 1
 
1.0%
3.448063288 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.018607568 1
1.0%
0.021169821 1
1.0%
0.045302262 1
1.0%
0.100682852 1
1.0%
0.102020755 1
1.0%
0.131955444 1
1.0%
0.159486315 1
1.0%
0.165871627 1
1.0%
0.226410361 1
1.0%
0.333431825 1
1.0%
ValueCountFrequency (%)
4.939255289 1
1.0%
4.911095955 1
1.0%
4.854068026 1
1.0%
4.843456577 1
1.0%
4.754800805 1
1.0%
4.746648621 1
1.0%
4.620546065 1
1.0%
4.580592619 1
1.0%
4.548919659 1
1.0%
4.367470538 1
1.0%

trans_modes
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Road
29 
Rail
28 
Air
26 
Sea
17 

Length

Max length4
Median length4
Mean length3.57
Min length3

Characters and Unicode

Total characters357
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoad
2nd rowRoad
3rd rowAir
4th rowRail
5th rowAir

Common Values

ValueCountFrequency (%)
Road 29
29.0%
Rail 28
28.0%
Air 26
26.0%
Sea 17
17.0%

Length

2025-10-01T23:02:00.669289image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T23:02:00.734664image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
road 29
29.0%
rail 28
28.0%
air 26
26.0%
sea 17
17.0%

Most occurring characters

ValueCountFrequency (%)
a 74
20.7%
R 57
16.0%
i 54
15.1%
o 29
 
8.1%
d 29
 
8.1%
l 28
 
7.8%
A 26
 
7.3%
r 26
 
7.3%
S 17
 
4.8%
e 17
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 74
20.7%
R 57
16.0%
i 54
15.1%
o 29
 
8.1%
d 29
 
8.1%
l 28
 
7.8%
A 26
 
7.3%
r 26
 
7.3%
S 17
 
4.8%
e 17
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 74
20.7%
R 57
16.0%
i 54
15.1%
o 29
 
8.1%
d 29
 
8.1%
l 28
 
7.8%
A 26
 
7.3%
r 26
 
7.3%
S 17
 
4.8%
e 17
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 357
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 74
20.7%
R 57
16.0%
i 54
15.1%
o 29
 
8.1%
d 29
 
8.1%
l 28
 
7.8%
A 26
 
7.3%
r 26
 
7.3%
S 17
 
4.8%
e 17
 
4.8%

Routes
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Route A
43 
Route B
37 
Route C
20 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters700
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoute B
2nd rowRoute B
3rd rowRoute C
4th rowRoute A
5th rowRoute A

Common Values

ValueCountFrequency (%)
Route A 43
43.0%
Route B 37
37.0%
Route C 20
20.0%

Length

2025-10-01T23:02:00.821939image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T23:02:00.882316image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
ValueCountFrequency (%)
route 100
50.0%
a 43
21.5%
b 37
 
18.5%
c 20
 
10.0%

Most occurring characters

ValueCountFrequency (%)
R 100
14.3%
o 100
14.3%
u 100
14.3%
t 100
14.3%
e 100
14.3%
100
14.3%
A 43
6.1%
B 37
 
5.3%
C 20
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 100
14.3%
o 100
14.3%
u 100
14.3%
t 100
14.3%
e 100
14.3%
100
14.3%
A 43
6.1%
B 37
 
5.3%
C 20
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 100
14.3%
o 100
14.3%
u 100
14.3%
t 100
14.3%
e 100
14.3%
100
14.3%
A 43
6.1%
B 37
 
5.3%
C 20
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 100
14.3%
o 100
14.3%
u 100
14.3%
t 100
14.3%
e 100
14.3%
100
14.3%
A 43
6.1%
B 37
 
5.3%
C 20
 
2.9%

other_cost
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean529.24578
Minimum103.91625
Maximum997.41345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:02:00.985244image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum103.91625
5-th percentile134.04373
Q1318.77846
median520.43044
Q3763.07823
95-th percentile923.73036
Maximum997.41345
Range893.4972
Interquartile range (IQR)444.29978

Descriptive statistics

Standard deviation258.3017
Coefficient of variation (CV)0.48805622
Kurtosis-1.1693231
Mean529.24578
Median Absolute Deviation (MAD)239.5189
Skewness0.040144408
Sum52924.578
Variance66719.766
MonotonicityNot monotonic
2025-10-01T23:02:01.127641image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
187.7520755 1
 
1.0%
687.2861779 1
 
1.0%
866.4728001 1
 
1.0%
677.9445698 1
 
1.0%
405.1670679 1
 
1.0%
183.2728987 1
 
1.0%
207.6632062 1
 
1.0%
299.7063031 1
 
1.0%
169.2718014 1
 
1.0%
393.8433486 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
103.916248 1
1.0%
110.3643352 1
1.0%
123.4370275 1
1.0%
126.7230334 1
1.0%
127.8618 1
1.0%
134.3690969 1
1.0%
141.9202818 1
1.0%
164.3665282 1
1.0%
169.2718014 1
1.0%
183.2728987 1
1.0%
ValueCountFrequency (%)
997.4134501 1
1.0%
996.778315 1
1.0%
995.9294615 1
1.0%
990.0784725 1
1.0%
929.23529 1
1.0%
923.4406317 1
1.0%
882.1988635 1
1.0%
880.0809882 1
1.0%
879.3592177 1
1.0%
873.129648 1
1.0%

revenue
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22855.5
Minimum90.557466
Maximum87098.044
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:02:01.258481image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum90.557466
5-th percentile865.77599
Q15489.6645
median13134.49
Q337071.66
95-th percentile63355.905
Maximum87098.044
Range87007.486
Interquartile range (IQR)31581.995

Descriptive statistics

Standard deviation22846.027
Coefficient of variation (CV)0.99958556
Kurtosis0.31085489
Mean22855.5
Median Absolute Deviation (MAD)10132.709
Skewness1.1251184
Sum2285550
Variance5.2194096 × 108
MonotonicityNot monotonic
2025-10-01T23:02:01.372117image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55986.02044 1
 
1.0%
3202.274689 1
 
1.0%
31444.13163 1
 
1.0%
43117.71635 1
 
1.0%
4065.036625 1
 
1.0%
1533.265338 1
 
1.0%
28036.28507 1
 
1.0%
6182.885418 1
 
1.0%
45018.5368 1
 
1.0%
18328.54832 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
90.55746634 1
1.0%
218.6188981 1
1.0%
249.896474 1
1.0%
265.0916361 1
1.0%
810.8193128 1
1.0%
868.6684471 1
1.0%
944.526234 1
1.0%
1533.265338 1
1.0%
1690.817404 1
1.0%
1924.07486 1
1.0%
ValueCountFrequency (%)
87098.04365 1
1.0%
87010.04158 1
1.0%
80386.10032 1
1.0%
79463.8936 1
1.0%
75145.14875 1
1.0%
62735.41828 1
1.0%
61862.96003 1
1.0%
56894.56036 1
1.0%
56362.06616 1
1.0%
55986.02044 1
1.0%

total_cost
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean582.06062
Minimum139.33493
Maximum1083.4289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:02:01.504433image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum139.33493
5-th percentile185.23675
Q1354.51552
median571.23474
Q3801.36769
95-th percentile950.45209
Maximum1083.4289
Range944.09394
Interquartile range (IQR)446.85217

Descriptive statistics

Standard deviation259.67327
Coefficient of variation (CV)0.44612753
Kurtosis-1.1087261
Mean582.06062
Median Absolute Deviation (MAD)224.13931
Skewness0.060652056
Sum58206.062
Variance67430.206
MonotonicityNot monotonic
2025-10-01T23:02:01.617987image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
236.9885268 1
 
1.0%
709.8191154 1
 
1.0%
914.8559758 1
 
1.0%
740.3046769 1
 
1.0%
445.0569357 1
 
1.0%
201.0293983 1
 
1.0%
294.9962588 1
 
1.0%
398.0141365 1
 
1.0%
185.4774891 1
 
1.0%
438.143928 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
139.3349287 1
1.0%
158.8393396 1
1.0%
175.9024202 1
1.0%
180.3501889 1
1.0%
180.6627804 1
1.0%
185.4774891 1
1.0%
192.7645498 1
1.0%
201.0293983 1
1.0%
211.8987755 1
1.0%
219.8089008 1
1.0%
ValueCountFrequency (%)
1083.428867 1
1.0%
1068.791586 1
1.0%
1051.053708 1
1.0%
1044.022712 1
1.0%
1019.39634 1
1.0%
946.8234469 1
1.0%
940.6704099 1
1.0%
937.0664968 1
1.0%
923.6683284 1
1.0%
920.8653442 1
1.0%

profit
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22273.439
Minimum-395.18353
Maximum86851.202
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)3.0%
Memory size932.0 B
2025-10-01T23:02:01.733858image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-395.18353
5-th percentile238.07446
Q15012.3463
median12698.886
Q336651.352
95-th percentile62326.143
Maximum86851.202
Range87246.386
Interquartile range (IQR)31639.006

Descriptive statistics

Standard deviation22859.577
Coefficient of variation (CV)1.0263155
Kurtosis0.30796421
Mean22273.439
Median Absolute Deviation (MAD)10233.498
Skewness1.1242239
Sum2227343.9
Variance5.2256025 × 108
MonotonicityNot monotonic
2025-10-01T23:02:01.901080image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55749.03192 1
 
1.0%
2492.455574 1
 
1.0%
30529.27565 1
 
1.0%
42377.41168 1
 
1.0%
3619.979689 1
 
1.0%
1332.23594 1
 
1.0%
27741.28881 1
 
1.0%
5784.871282 1
 
1.0%
44833.05931 1
 
1.0%
17890.40439 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
-395.1835263 1
1.0%
-90.10531404 1
1.0%
-46.77533712 1
1.0%
59.8200231 1
1.0%
125.7567074 1
1.0%
243.9859163 1
1.0%
751.7616842 1
1.0%
1003.209516 1
1.0%
1104.912378 1
1.0%
1166.384744 1
1.0%
ValueCountFrequency (%)
86851.20224 1
1.0%
86487.74955 1
1.0%
79765.40392 1
1.0%
78596.31934 1
1.0%
74519.93103 1
1.0%
61684.36457 1
1.0%
61065.49455 1
1.0%
56061.43196 1
1.0%
55850.53765 1
1.0%
55749.03192 1
1.0%

profit_margin
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008455634
Minimum-0.018076366
Maximum0.009981745
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)3.0%
Memory size932.0 B
2025-10-01T23:02:02.085425image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-0.018076366
5-th percentile0.0046571576
Q10.0089047375
median0.009534822
Q30.0098383265
95-th percentile0.0099472105
Maximum0.009981745
Range0.028058111
Interquartile range (IQR)0.000933589

Descriptive statistics

Standard deviation0.0037697033
Coefficient of variation (CV)0.44582149
Kurtosis30.222164
Mean0.008455634
Median Absolute Deviation (MAD)0.0003563855
Skewness-5.1277543
Sum0.8455634
Variance1.4210663 × 10-5
MonotonicityNot monotonic
2025-10-01T23:02:02.225673image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00995767 1
 
1.0%
0.007783391 1
 
1.0%
0.009709054 1
 
1.0%
0.009828306 1
 
1.0%
0.008905159 1
 
1.0%
0.008688881 1
 
1.0%
0.009894781 1
 
1.0%
0.009356265 1
 
1.0%
0.0099588 1
 
1.0%
0.00976095 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
-0.018076366 1
1.0%
-0.00995007 1
1.0%
-0.001871789 1
1.0%
0.00068864 1
1.0%
0.003009128 1
1.0%
0.004743896 1
1.0%
0.005213984 1
1.0%
0.005519498 1
1.0%
0.005661184 1
1.0%
0.006377039 1
1.0%
ValueCountFrequency (%)
0.009981745 1
1.0%
0.009968291 1
1.0%
0.009966364 1
1.0%
0.0099588 1
1.0%
0.00995767 1
1.0%
0.00994666 1
1.0%
0.009941778 1
1.0%
0.009935984 1
1.0%
0.009931655 1
1.0%
0.00992993 1
1.0%

no_orders_notful
Real number (ℝ)

High correlation 

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-411.77
Minimum-942
Maximum80
Zeros0
Zeros (%)0.0%
Negative97
Negative (%)97.0%
Memory size932.0 B
2025-10-01T23:02:02.392891image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-942
5-th percentile-900.15
Q1-663.25
median-363
Q3-140.25
95-th percentile-10
Maximum80
Range1022
Interquartile range (IQR)523

Descriptive statistics

Standard deviation304.53159
Coefficient of variation (CV)-0.73956722
Kurtosis-1.2455635
Mean-411.77
Median Absolute Deviation (MAD)257
Skewness-0.28320502
Sum-41177
Variance92739.492
MonotonicityNot monotonic
2025-10-01T23:02:02.581856image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-201 2
 
2.0%
-90 2
 
2.0%
-24 2
 
2.0%
-292 2
 
2.0%
-81 2
 
2.0%
-10 2
 
2.0%
-831 2
 
2.0%
-484 2
 
2.0%
-236 1
 
1.0%
-611 1
 
1.0%
Other values (82) 82
82.0%
ValueCountFrequency (%)
-942 1
1.0%
-935 1
1.0%
-916 1
1.0%
-915 1
1.0%
-903 1
1.0%
-900 1
1.0%
-897 1
1.0%
-895 1
1.0%
-894 1
1.0%
-886 1
1.0%
ValueCountFrequency (%)
80 1
1.0%
41 1
1.0%
28 1
1.0%
-7 1
1.0%
-10 2
2.0%
-20 1
1.0%
-24 2
2.0%
-41 1
1.0%
-48 1
1.0%
-50 1
1.0%

stockout_rate
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.24533775
Minimum-9.03
Maximum0.009090909
Zeros0
Zeros (%)0.0%
Negative97
Negative (%)97.0%
Memory size932.0 B
2025-10-01T23:02:02.795061image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum-9.03
5-th percentile-0.853
Q1-0.17019608
median-0.075667756
Q3-0.033264706
95-th percentile-0.003924752
Maximum0.009090909
Range9.0390909
Interquartile range (IQR)0.13693137

Descriptive statistics

Standard deviation0.91982184
Coefficient of variation (CV)-3.7492063
Kurtosis86.237748
Mean-0.24533775
Median Absolute Deviation (MAD)0.054047619
Skewness-9.0136471
Sum-24.533775
Variance0.84607222
MonotonicityNot monotonic
2025-10-01T23:02:02.933534image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.073541667 1
 
1.0%
-0.236 1
 
1.0%
-0.046818182 1
 
1.0%
-0.052077922 1
 
1.0%
-0.235 1
 
1.0%
-0.004545455 1
 
1.0%
-0.179259259 1
 
1.0%
-0.018103448 1
 
1.0%
-0.062253521 1
 
1.0%
-0.360526316 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
-9.03 1
1.0%
-1.421666667 1
1.0%
-1.035 1
1.0%
-0.9575 1
1.0%
-0.937142857 1
1.0%
-0.848571429 1
1.0%
-0.735454545 1
1.0%
-0.627272727 1
1.0%
-0.448571429 1
1.0%
-0.377272727 1
1.0%
ValueCountFrequency (%)
0.009090909 1
1.0%
0.006212121 1
1.0%
0.005384615 1
1.0%
-0.001204819 1
1.0%
-0.001206897 1
1.0%
-0.004067797 1
1.0%
-0.004363636 1
1.0%
-0.004545455 1
1.0%
-0.004823529 1
1.0%
-0.007272727 1
1.0%

fill_rate
Real number (ℝ)

High correlation  Unique 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25533775
Minimum0.000909091
Maximum9.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size932.0 B
2025-10-01T23:02:03.084388image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Quantile statistics

Minimum0.000909091
5-th percentile0.013924752
Q10.043264706
median0.085667756
Q30.18019608
95-th percentile0.863
Maximum9.04
Range9.0390909
Interquartile range (IQR)0.13693137

Descriptive statistics

Standard deviation0.91982184
Coefficient of variation (CV)3.602373
Kurtosis86.237748
Mean0.25533775
Median Absolute Deviation (MAD)0.054047619
Skewness9.0136471
Sum25.533775
Variance0.84607222
MonotonicityNot monotonic
2025-10-01T23:02:03.242442image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.083541667 1
 
1.0%
0.246 1
 
1.0%
0.056818182 1
 
1.0%
0.062077922 1
 
1.0%
0.245 1
 
1.0%
0.014545455 1
 
1.0%
0.189259259 1
 
1.0%
0.028103448 1
 
1.0%
0.072253521 1
 
1.0%
0.370526316 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.000909091 1
1.0%
0.003787879 1
1.0%
0.004615385 1
1.0%
0.011204819 1
1.0%
0.011206897 1
1.0%
0.014067797 1
1.0%
0.014363636 1
1.0%
0.014545455 1
1.0%
0.014823529 1
1.0%
0.017272727 1
1.0%
ValueCountFrequency (%)
9.04 1
1.0%
1.431666667 1
1.0%
1.045 1
1.0%
0.9675 1
1.0%
0.947142857 1
1.0%
0.858571429 1
1.0%
0.745454545 1
1.0%
0.637272727 1
1.0%
0.458571429 1
1.0%
0.387272727 1
1.0%

Interactions

2025-10-01T23:01:51.621438image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:09.305665image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:10.996557image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:12.933675image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.674868image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.404720image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:18.251835image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:20.109191image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:22.052548image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:24.002749image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:25.910051image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:28.065768image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:30.263642image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:32.645409image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.708043image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:36.935611image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:38.925795image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:40.873420image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:43.030599image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:45.130658image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:47.180417image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.540928image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:51.706441image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:09.382071image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:11.088276image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:13.008686image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.768969image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.479088image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:18.332556image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:20.179003image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:22.119802image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:24.071628image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:26.023363image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:28.187262image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:30.389257image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:32.722149image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.811749image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:37.019656image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:39.016455image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:40.951616image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:43.114631image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:45.235373image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:47.261740image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.641434image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:51.806640image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:09.461112image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:11.207164image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:13.106706image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.839547image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.548723image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:18.414677image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:20.264277image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:22.196576image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:24.150003image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:26.088738image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:28.290999image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:30.497574image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:32.799399image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.923217image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:37.129776image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:39.090637image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:41.060792image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:43.190074image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:45.339064image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:47.397525image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.714435image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:51.876817image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:09.541358image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:11.282085image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:13.183721image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.921261image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.626710image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:18.486236image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:20.366449image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:22.262079image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:24.218469image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:26.215562image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:28.420364image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:30.619979image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:32.893568image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.995032image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:37.207618image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:39.157484image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:41.138342image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:43.289077image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:45.427693image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:47.531139image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.784485image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:51.994956image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:09.612267image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:11.353686image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:13.274516image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.993898image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.705644image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:18.539305image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:20.428210image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:22.339242image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:24.302797image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:26.307793image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:28.557640image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:30.754770image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:32.970115image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:35.071417image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:37.275079image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:39.260031image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:41.226810image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:43.383101image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:45.525120image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:47.660015image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.875459image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:52.066032image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:09.702235image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:11.428773image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:13.358767image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:15.087118image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.778072image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:18.581185image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:20.540964image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:22.443980image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:24.377271image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:26.405874image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:28.644010image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:30.833968image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:33.066519image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:35.148684image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:37.371588image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:39.352698image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:41.309675image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:43.481100image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:45.612150image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:47.760445image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.985660image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:52.140173image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:09.779019image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:11.668441image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:13.443163image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:15.164027image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.854863image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:18.630046image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
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2025-10-01T23:01:27.389232image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:29.646753image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:31.853796image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.057841image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
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2025-10-01T23:01:38.324330image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:40.223334image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:42.433134image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:44.467002image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:46.492460image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:48.904712image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:50.948119image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:53.033014image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:10.543607image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:12.507985image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.239092image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:15.966802image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:17.750874image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:19.606808image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:21.497051image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:23.540838image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:25.429390image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:27.510666image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:29.770430image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:31.968748image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.143604image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:36.368162image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:38.404568image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:40.305461image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
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2025-10-01T23:01:44.536634image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:46.570244image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:48.999148image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:51.054401image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:53.101383image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:10.633386image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:12.579167image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.312046image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.032990image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:17.836699image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:19.683462image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:21.606623image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:23.612849image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:25.506172image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:27.631135image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:29.848403image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:32.093843image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.235633image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:36.437474image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:38.492501image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:40.419569image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:42.650639image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:44.616144image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:46.702724image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.086054image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:51.171059image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:53.184562image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:10.708017image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:12.646638image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.387289image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.104517image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:17.907024image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:19.765808image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:21.705902image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:23.701864image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:25.596431image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:27.704499image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:29.941345image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:32.214907image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.345281image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:36.565794image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:38.579898image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:40.515040image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:42.711229image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:44.709283image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:46.788506image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.162335image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:51.263489image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:53.283591image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:10.779646image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:12.726738image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.461624image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.181883image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:17.985356image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:19.870046image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:21.788768image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:23.784697image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:25.669582image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:27.814616image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:30.035951image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:32.320079image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.450360image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:36.663795image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:38.663518image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:40.586250image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:42.801819image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:44.822811image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:46.882148image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.260174image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:51.344793image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:53.355248image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:10.850792image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:12.795628image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.534190image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.255444image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:18.057007image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:19.952781image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:21.874026image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:23.863995image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:25.765432image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:27.900043image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:30.117180image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:32.446136image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.563934image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:36.749691image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:38.767385image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:40.677915image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:42.866038image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:44.915409image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:46.993231image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.345623image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:51.457495image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:53.413800image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:10.927641image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:12.862481image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:14.607568image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:16.332302image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:18.163767image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:20.038007image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:21.982715image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:23.932476image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:25.839693image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:27.976696image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:30.188620image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:32.548373image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:34.646476image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:36.859179image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:38.848643image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:40.781979image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:42.947594image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:45.012235image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:47.093125image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:49.468176image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
2025-10-01T23:01:51.562340image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/

Correlations

2025-10-01T23:02:03.375134image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
PriceRoutesavail_percustomer_ltdef_ratedelv_locationfill_rategenderinspect_reslmanfuct_costmanfuct_ltno_orders_notfulno_prod_soldorder_qunother_costp_typeprod_volumprofitprofit_marginrev_genrevenueshipping_carrshipping_costshipping_ltstock_perstockout_ratesup_namesuppling_lttotal_costtrans_modes
Price1.0000.0890.0000.038-0.1490.125-0.0840.0000.000-0.187-0.291-0.0040.0070.1020.0960.260-0.1170.7060.6340.0320.7100.0000.0450.0750.0930.0840.0000.1550.0830.108
Routes0.0891.0000.1490.2410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0860.1170.0000.1380.2290.0000.0000.2100.0000.0000.0810.0000.0000.0000.0000.0000.000
avail_per0.0000.1491.0000.1690.0290.000-0.0140.1150.1580.1420.077-0.0520.0680.128-0.0420.0000.0710.0690.061-0.0770.0740.171-0.056-0.039-0.0480.0140.176-0.186-0.0370.000
customer_lt0.0380.2410.1691.0000.0050.090-0.0390.0000.000-0.020-0.0110.035-0.0310.1190.2460.114-0.1370.035-0.057-0.0690.0410.054-0.118-0.0280.0950.0390.0000.0050.2440.036
def_rate-0.1490.0000.0290.0051.0000.000-0.1070.0990.0160.0080.1200.087-0.0860.0060.0400.0000.117-0.144-0.142-0.130-0.1430.0680.082-0.035-0.1530.1070.0000.2800.0370.222
delv_location0.1250.0000.0000.0900.0001.0000.1100.0000.0000.1750.0000.2110.1930.0340.0910.0000.0880.0000.0000.0000.0000.0000.0610.0000.0760.1100.0000.0000.1090.063
fill_rate-0.0840.000-0.014-0.039-0.1070.1101.0000.0000.0050.050-0.138-0.7920.736-0.582-0.1140.1780.1520.4270.445-0.0090.4220.0580.0380.1090.127-1.0000.0840.045-0.1150.000
gender0.0000.0000.1150.0000.0990.0000.0001.0000.1400.0000.0000.0000.1470.2020.0810.1890.0000.1410.0200.2170.1130.0000.0000.0000.1310.0000.0300.0000.1510.000
inspect_resl0.0000.0000.1580.0000.0160.0000.0050.1401.0000.0000.2570.0670.1870.0000.0000.0000.0000.0000.0000.0000.0000.1580.0000.0000.0000.0050.2790.0000.0000.000
manfuct_cost-0.1870.0000.142-0.0200.0080.1750.0500.0000.0001.000-0.137-0.0350.035-0.035-0.0250.2480.079-0.139-0.140-0.222-0.1370.0000.0190.0370.026-0.0500.164-0.1240.0900.000
manfuct_lt-0.2910.0000.077-0.0110.1200.000-0.1380.0000.257-0.1371.0000.088-0.0770.104-0.0940.0810.195-0.218-0.1850.009-0.2220.000-0.0070.020-0.0650.1380.0000.015-0.1160.000
no_orders_notful-0.0040.000-0.0520.0350.0870.211-0.7920.0000.067-0.0350.0881.000-0.9940.0650.0500.152-0.179-0.625-0.607-0.004-0.6210.048-0.046-0.099-0.0630.7920.099-0.0590.0520.102
no_prod_sold0.0070.0000.068-0.031-0.0860.1930.7360.1470.1870.035-0.077-0.9941.0000.027-0.0350.1610.1670.6340.6090.0020.6310.0000.0500.0900.056-0.7360.1750.046-0.0360.000
order_qun0.1020.0860.1280.1190.0060.034-0.5820.2020.000-0.0350.1040.0650.0271.0000.1530.137-0.0640.0970.0340.0710.1060.000-0.0040.003-0.0920.5820.038-0.0860.1520.000
other_cost0.0960.117-0.0420.2460.0400.091-0.1140.0810.000-0.025-0.0940.050-0.0350.1531.0000.000-0.078-0.038-0.3120.033-0.0170.2230.041-0.023-0.0070.1140.0000.0490.9920.246
p_type0.2600.0000.0000.1140.0000.0000.1780.1890.0000.2480.0810.1520.1610.1370.0001.0000.0000.2100.0000.0000.2270.1070.1550.0000.1180.1780.1990.1610.0000.000
prod_volum-0.1170.1380.071-0.1370.1170.0880.1520.0000.0000.0790.195-0.1790.167-0.064-0.0780.0001.0000.0300.062-0.0310.0280.000-0.096-0.0640.029-0.1520.1550.188-0.0790.000
profit0.7060.2290.0690.035-0.1440.0000.4270.1410.000-0.139-0.218-0.6250.6340.097-0.0380.2100.0301.0000.9450.0090.9990.0000.0150.0980.143-0.4270.0000.157-0.0520.000
profit_margin0.6340.0000.061-0.057-0.1420.0000.4450.0200.000-0.140-0.185-0.6070.6090.034-0.3120.0000.0620.9451.000-0.0080.9400.0740.0030.1260.127-0.4450.0000.137-0.3270.000
rev_gen0.0320.000-0.077-0.069-0.1300.000-0.0090.2170.000-0.2220.009-0.0040.0020.0710.0330.000-0.0310.009-0.0081.0000.0070.000-0.092-0.125-0.1250.0090.066-0.0100.0100.000
revenue0.7100.2100.0740.041-0.1430.0000.4220.1130.000-0.137-0.222-0.6210.6310.106-0.0170.2270.0280.9990.9400.0071.0000.0000.0160.1020.135-0.4220.0000.154-0.0310.000
shipping_carr0.0000.0000.1710.0540.0680.0000.0580.0000.1580.0000.0000.0480.0000.0000.2230.1070.0000.0000.0740.0000.0001.0000.0000.1420.2160.0580.0000.1040.2070.145
shipping_cost0.0450.000-0.056-0.1180.0820.0610.0380.0000.0000.019-0.007-0.0460.050-0.0040.0410.155-0.0960.0150.003-0.0920.0160.0001.0000.0620.082-0.0380.0000.0080.0580.000
shipping_lt0.0750.081-0.039-0.028-0.0350.0000.1090.0000.0000.0370.020-0.0990.0900.003-0.0230.000-0.0640.0980.126-0.1250.1020.1420.0621.000-0.116-0.1090.000-0.052-0.0230.157
stock_per0.0930.000-0.0480.095-0.1530.0760.1270.1310.0000.026-0.065-0.0630.056-0.092-0.0070.1180.0290.1430.127-0.1250.1350.2160.082-0.1161.000-0.1270.0000.080-0.0040.000
stockout_rate0.0840.0000.0140.0390.1070.110-1.0000.0000.005-0.0500.1380.792-0.7360.5820.1140.178-0.152-0.427-0.4450.009-0.4220.058-0.038-0.109-0.1271.0000.084-0.0450.1150.000
sup_name0.0000.0000.1760.0000.0000.0000.0840.0300.2790.1640.0000.0990.1750.0380.0000.1990.1550.0000.0000.0660.0000.0000.0000.0000.0000.0841.0000.0000.0000.205
suppling_lt0.1550.000-0.1860.0050.2800.0000.0450.0000.000-0.1240.015-0.0590.046-0.0860.0490.1610.1880.1570.137-0.0100.1540.1040.008-0.0520.080-0.0450.0001.0000.0370.036
total_cost0.0830.000-0.0370.2440.0370.109-0.1150.1510.0000.090-0.1160.052-0.0360.1520.9920.000-0.079-0.052-0.3270.010-0.0310.2070.058-0.023-0.0040.1150.0000.0371.0000.260
trans_modes0.1080.0000.0000.0360.2220.0630.0000.0000.0000.0000.0000.1020.0000.0000.2460.0000.0000.0000.0000.0000.0000.1450.0000.1570.0000.0000.2050.0360.2601.000

Missing values

2025-10-01T23:01:53.599508image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-01T23:01:53.888829image/svg+xmlMatplotlib v3.10.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

p_typeSKUPriceavail_perno_prod_soldrev_gengenderstock_percustomer_ltorder_qunshipping_ltshipping_carrshipping_costsup_namedelv_locationsuppling_ltprod_volummanfuct_ltmanfuct_costinspect_resldef_ratetrans_modesRoutesother_costrevenuetotal_costprofitprofit_marginno_orders_notfulstockout_ratefill_rate
0haircareSKU069.808006558028661.996792Non-binary587964Carrier B2.956572Supplier 3Mumbai292152946.279879Pending0.226410RoadRoute B187.75207555986.020440236.98852755749.0319200.009958-706-0.0735420.083542
1skincareSKU114.843523957367460.900065Female5330372Carrier A9.716575Supplier 3Mumbai235173033.616769Pending4.854068RoadRoute B503.06557910924.833130546.39892310378.4342100.009500-699-0.1889190.198919
2haircareSKU211.3196833489577.749626Unknown110882Carrier B8.054479Supplier 1Mumbai129712730.688019Pending4.580593AirRoute C141.92028290.557466180.662780-90.105314-0.009950800.0090910.000909
3skincareSKU361.16334368837766.836426Non-binary2313596Carrier C1.729569Supplier 5Kolkata249371835.624741Fail4.746649RailRoute A254.7761595076.557470292.1304694784.4270010.009425-24-0.0040680.014068
4skincareSKU44.805496268712686.505152Non-binary53568Carrier A3.890548Supplier 1Delhi5414392.065161Fail3.145580AirRoute A923.4406324185.5870481019.3963403166.1907070.007565-815-0.1455360.155536
5haircareSKU51.699976871472828.348746Non-binary9027663Carrier B4.444099Supplier 4Bangalore101041756.766476Fail2.779194RoadRoute A235.461237249.896474296.671811-46.775337-0.001872-81-0.0122730.022273
6skincareSKU64.07833348657823.476560Male1115588Carrier C3.880763Supplier 3Kolkata14314241.085069Pending1.000911SeaRoute A134.369097265.091636139.334929125.7567070.004744-7-0.0012070.011207
7cosmeticsSKU742.958384594268496.103813Female9317111Carrier B2.348339Supplier 4Bangalore22564199.466109Fail0.398177RoadRoute C802.05631218300.271750903.87075917396.4009900.009506-415-0.3772730.387273
8cosmeticsSKU868.717597781507517.363211Female510157Carrier C3.404734Supplier 4Mumbai13769811.423027Pending2.709863SeaRoute B505.55713410307.639510520.3848959787.2546170.009495-135-0.0900000.100000
9skincareSKU964.015733359804971.145988Unknown1427831Carrier A7.166645Supplier 2Chennai299632347.957602Pending3.844614RailRoute B995.92946262735.4182801051.05370861684.3645700.009832-897-0.1080720.118072
p_typeSKUPriceavail_perno_prod_soldrev_gengenderstock_percustomer_ltorder_qunshipping_ltshipping_carrshipping_costsup_namedelv_locationsuppling_ltprod_volummanfuct_ltmanfuct_costinspect_resldef_ratetrans_modesRoutesother_costrevenuetotal_costprofitprofit_marginno_orders_notfulstockout_ratefill_rate
90skincareSKU9013.881913563209592.633570Non-binary6618967Carrier B7.674431Supplier 3Bangalore8585885.675963Pass1.219382RailRoute B990.0784724442.2123201083.4288673358.7834540.007561-224-0.0233330.033333
91cosmeticsSKU9162.111965909161935.206794Male9822857Carrier B7.471514Supplier 4Delhi52072839.772883Pending0.626002RailRoute B996.77831556894.5603601044.02271255850.5376500.009816-831-0.0977650.107765
92cosmeticsSKU9247.714233442762100.129755Male9025108Carrier B4.469500Supplier 2Mumbai46712962.612690Pass0.333432RailRoute B230.09278213169.128330297.17497312871.9533600.009774-266-0.2660000.276000
93haircareSKU9369.290831881144531.402134Unknown6317661Carrier C7.006432Supplier 4Chennai218242035.633652Fail4.165782AirRoute A823.5238467899.154734866.1639307032.9908040.008903-48-0.0072730.017273
94cosmeticsSKU943.037689979877888.356547Unknown7726729Carrier B6.942946Supplier 2Delhi129081460.387379Pass1.463607RailRoute B846.6652572998.198771913.9955812084.2031900.006952-915-0.1270830.137083
95haircareSKU9577.903927656727386.363944Unknown1514269Carrier B8.630339Supplier 4Mumbai184502658.890686Pending1.210882AirRoute A778.86424152351.439090846.38526651505.0538300.009838-646-0.2484620.258462
96cosmeticsSKU9624.423131293247698.424766Non-binary672323Carrier C5.352878Supplier 3Mumbai286482817.803756Pending3.872048RoadRoute A188.7421417913.094580211.8987757701.1958050.009732-292-0.0912500.101250
97haircareSKU973.52611156624370.916580Male461949Carrier A7.904846Supplier 4Mumbai105351365.765156Fail3.376238RoadRoute A540.132423218.618898613.802424-395.183526-0.018076-58-0.1450000.155000
98skincareSKU9819.754605439138525.952560Female531277Carrier B1.409801Supplier 5Chennai2858195.604691Pending2.908122RailRoute A882.19886418035.954250889.21335617146.7408900.009507-886-0.3281480.338148
99haircareSKU9968.517833176279185.185829Unknown558596Carrier B1.311024Supplier 2Chennai29921238.072899Fail0.346027RailRoute B210.74300942960.681100250.12693142710.5541700.009942-568-0.0962710.106271